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How Informative are the Subjective Density Forecasts of Macroeconomists?

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  • Geoff Kenny
  • Thomas Kostka
  • Federico Masera

Abstract

In this paper, we propose a framework to evaluate the information content of subjective expert density forecasts using micro data from the ECB’s Survey of Professional Forecasters (SPF). A key aspect of our analysis is the use of scoring functions which evaluate the entire predictive densities, including an evaluation of the impact of density features such as their location, spread, skew and tail risk on density forecast performance. Overall, we find considerable heterogeneity in the performance of the surveyed densities at the individual level. Relative to a set of crude benchmark alternatives, this performance is somewhat better for GDP growth than for inflation, although in the former case it diminishes substantially with the forecast horizon. In addition, relative to the proposed benchmarks, we report evidence of some improvement in the performance of expert densities during the recent period of macroeconomic volatility. However, our analysis also reveals clear evidence of overconfidence or neglected risks in the expert probability assessments, as reflected also in frequent occurrences of events which are assigned a zero probability. Moreover, higher moment features of the expert densities, such as their skew or the degree of probability mass in their tails, are shown not to contribute significantly to improvements in individual density forecast performance.

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Bibliographic Info

Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 3671.

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Date of creation: 2011
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Handle: RePEc:ces:ceswps:_3671

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Keywords: density forecasts; forecast evaluation; real-time data; Survey of Professional Forecasters;

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References

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  1. Victor Zarnowitz & Louis A. Lambros, 1983. "Consensus and Uncertainty in Economic Prediction," NBER Working Papers 1171, National Bureau of Economic Research, Inc.
  2. Geweke, John & Amisano, Gianni, 2009. "Optimal Prediction Pools," Working Paper Series 1017, European Central Bank.
  3. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
  4. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
  5. repec:nsr:niesrd:320 is not listed on IDEAS
  6. Robert Rich & Joseph Tracy, 2010. "The Relationships among Expected Inflation, Disagreement, and Uncertainty: Evidence from Matched Point and Density Forecasts," The Review of Economics and Statistics, MIT Press, vol. 92(1), pages 200-207, February.
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Cited by:
  1. Paloviita, Maritta & Viren, Matti, 2012. "Inflation and output growth uncertainty in individual survey expectations," Research Discussion Papers 37/2012, Bank of Finland.
  2. Kenny, Geoff & Kostka, Thomas & Masera, Federico, 2013. "Can macroeconomists forecast risk? Event-based evidence from the euro area SPF," Working Paper Series 1540, European Central Bank.
  3. Robert Rich & Joseph Song & Joseph Tracy, 2012. "The measurement and behavior of uncertainty: evidence from the ECB Survey of Professional Forecasters," Staff Reports 588, Federal Reserve Bank of New York.
  4. Maritta Paloviita and Matti Viren, 2012. "Are individual survey expectations internally consistent?," Discussion Papers 77, Aboa Centre for Economics.
  5. Clements, Michael P, 2012. "Subjective and Ex Post Forecast Uncertainty : US Inflation and Output Growth," The Warwick Economics Research Paper Series (TWERPS) 995, University of Warwick, Department of Economics.
  6. Paloviita, Maritta & Viren, Matti, 2014. "Analysis of forecast errors in micro-level survey data," Research Discussion Papers 8/2014, Bank of Finland.

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